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A chatbot is the most in-your-face use case of AI, but it’s easy to underestimate the opportunities that AI can help us realize. By some estimates, by 2023 around 40% of all internal operations teams in Enterprises will be AI-enabled. The flip side is that even though the growth opportunities are huge, it will take time, effort, and a concerted strategy to realize the true potential.

Let us look at the key considerations to factor in while embarking on the AI journey.

Definite Use Cases:

It is imperative to have a definite use case in mind before one thinks of implementing AI in your Enterprise. Many implementations fail simply because they are implemented with no thought about the end goal to be achieved. To avail a great ROI, it is extremely important that one has a clear definition of the specific business goals to shoot for. For instance, a customer service operation may want to reduce the number of customer service calls by a factor of 50%. Chatbot-enabled engines could help -and after a defined period you can establish clearly if the initiative was a success.

Think Big Start Small:

It is best to have lofty goals while aiming for a transformation with AI but start with a small test or a pilot project. It’s always prudent to test the waters before taking the plunge. Chose one particular LOB, or a small department to test AI and its viability for this particular endeavor. This will throw up the problems one can encounter while undergoing a transformation. And at the same time, you will also identify the challenges resident within the ecosystem that may have to be addressed for achieving a seamless transformation.

Creation of a Knowledge Repository:

The success of an AI implementation is dependent on how robust the underlying knowledge base is. This requires data, lots of it. The AI will learn as it goes along -but even at the stage of training the AI, vast amounts of data is needed. The idea is to have the AI system define how a problem can be solved and be driven by the relevant insights the AI provides. By having a highly mature algorithm driven by a robust database you can improve the quality of the insights available. The primary difference between a normal knowledge repository and a Knowledge repository for AI is in the structure and the content. For AI, an interface along with highly structured data which can be queried is necessary.

Build or Buy and choosing the Correct Partner:

AI may be necessary for every organization but not every organization will have the requisite resources to implement it on their own. You could build the expertise, or you may have to work with a partner.Picking the right partner is a crucial decision. The selection should be driven by considerations like the availability of skilled human resources, successful past implementation,understanding of your business challenges, and their future roadmap.

Data Quality:

For AI data quantity is not enough, data quality is paramount. AI is driven by Data Science and statistical algorithms. These algorithms become trustworthy if the data quality of the data set on which the system is being trained and implemented is pure and pristine. That is the reason why there should be a state-of-the-art data quality monitoring system. You may have to fix the data duplication issues and weed out the corrupt and broken data.

Cloud or On-Premise:

Once put into place, the knowledge repository will increase in size at an exponential rate. A tsunami of streaming data will fill up the data storage really fast. Hence many organizations consider the cloud for storing the data. The answer to the question of whether to go for cloud or stay on-premise will be driven by factors like the security and compliance requirements, apart from the cost and storage volume needed.

Right Resource Pool:

Irrespective of the decision to build or buy it’s true that there are not many trained and experienced human resources out there. It is common to underestimate the demands AI will make on the business. This is not just about the technical resources needed to implement the systems. AI strategies sometimes fall apart because the Enterprise didn’t train or develop their functional resources to cater to the new ways of working. Business processes will change, agility will increase, and responsibilities will shift -your people will have to be ready.

Top Management Buy-in:

Like any other strategic initiative, the involvement of the top management is a key factor for the success of any AI implementation. Many Enterprises still work top-down. With top management throwing its weight behind a project, the probability of its success increases exponentially. The organization starts treating the implementation with the required seriousness. Resources get allocated, Results get tracked.

Conclusion:

As you can see, there are quite a few factors to bake into the implementation of your Enterprise AI initiative. Knowing these factors and staying hyper-focused will help you stay on track with your AI initiative. And implementing a robust AI strategy that has the greatest chance of delivering business impact is what it’s all about -isn’t it?

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